Regression model selection—a residual likelihood approach

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Tuning parameter selectors for the smoothly clipped absolute deviation method. (2007, methodology): “Techniques similar to those used above show that AIC performs like generalized crossvalidation, with a potential for overfitting, while RIC consistently identifies the true model.”
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso (2007, background): “(For a better explanation of model selection efﬁciency and consistency, see Shao 1997; McQuarrie and Tsai 1998; and Shi and Tsai 2002.)”
A note on shrinkage sliced inverse regression (2005, unknown): “…consider Akaike's information criterion (Akaike, 1973), the Bayesian information criterion (Schwarz, 1978), and the residual information criterion (Shi & Tsai, 2002): =n log (/n)+2p(l), =n log (/n)+log (n)p(l), ={n−p(l)} log (s @ 2)+p(l){log (n)−1}+4/{n−p(l)−2}, where s @…”
Shrinkage tuning parameter selection with a diverging number of parameters (2009, methodology): “Thus, the traditional theoretical arguments (e.g. Shao (1997), Shi and Tsai (2002) and Wang et al. (2007b)) are no longer applicable.”
Selecting the Best Linear Mixed Model Under REML (2006, background): “Additionally, Shi and Tsai (2002) raised the point that information criteria under ML are calculated from biased estimators and hence may not be suitable.”